I would like to know the history and motivations for the changes of Pydantic over time in the Python ecosystem. Please look at the conversations on Talk Python to Me, the podcast, to learn more about why Samuel Colvin and others evolved Pydantic the way that they have.
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I have comprehensive information about Pydantic's history and evolution. Let me compile this into a answer for you.
Based on conversations from Talk Python to Me with Samuel Colvin (Pydantic creator), Sebastián Ramírez (FastAPI creator), and Sydney Runkle (Pydantic team member), here's the story of how Pydantic evolved:
Samuel Colvin created Pydantic originally for himself, not anticipating widespread adoption. As he described in Episode 313:
"I literally built Pydantic for me and put it on PyPI... to see what would happen."
His background is notably unconventional—he discovered programming while working on oil rigs in Indonesia, where long shifts and downtime gave him time to explore coding.
Several factors contributed to Pydantic's explosive growth:
Type hints were maturing: Python type hints existed but "didn't have teeth"—they were documentation only. Coming from Rust/C++, Samuel found it strange that types meant nothing at runtime.
Developer-first pragmatism: Samuel approached validation as a developer, not a "typing academic." His philosophy was clear: a string "123" should coerce to an integer—that's what developers actually need when parsing URLs and JSON.
FastAPI adoption: Sebastián Ramírez built FastAPI on top of Pydantic, which "strapped rockets" to Pydantic's adoption.
Excellent documentation: Early investment in clear, readable docs helped new users get started quickly.
By 2022, Pydantic had become critical infrastructure—72,000+ public repos depended on it, with 24 million monthly PyPI downloads. Samuel was proud of the adoption but "not quite so proud of its internals." This led to the V2 plan:
Why rewrite in Rust?
Performance at scale matters: Samuel made an interesting environmental argument—if Netflix, Facebook, Amazon, and Microsoft are all running Pydantic millions of times daily, making it 10x faster has real environmental impact by reducing CPU usage.
Recursion without penalty: Rust allows truly recursive validators without Python's function call overhead.
Built-in JSON parsing: Parsing JSON directly in Rust avoids creating intermediate Python objects, solving strict mode edge cases (like ISO date strings).
Key motivations for V2 changes:
Strict mode: Originally, coercion was always on. V2 added formal strict mode because some contexts demand zero unexpected conversions.
Smarter unions: Using strict mode first when validating unions solved edge cases like Union[int, str] receiving "123".
Aliases with deep paths: Flattening nested JSON structures became possible—you can pull data from foo["bar"]["baz"] directly into a top-level field.
"From attributes" replacing "from_orm": The original ORM integration was too narrow; the new approach validates any Python object via getattr.
In 2023, Samuel secured venture capital funding from Sequoia to build a company around Pydantic—rare for a Python open-source project. This allowed hiring a dedicated team (including Sydney Runkle) and led to:
Logfire: An observability platform built on OpenTelemetry
FastUI: React-based UIs defined entirely in Python using Pydantic models
Continued V2 refinement: Performance optimizations, better error messages with documentation links
Pydantic's evolution has rippled through the Python ecosystem:
FastAPI dropped internal workarounds and gained automatic speed improvements
Beanie (MongoDB ODM) and SQLModel let developers use Pydantic models as database schemas
Django Ninja brought Pydantic's API validation style to Django
Remember what it was like to be a beginner: Make the library easy to use, not a showcase for advanced knowledge
Practical over theoretical: Coerce when it makes sense; be strict when asked
Performance through architecture: Move compute-heavy work to Rust while keeping Python's ergonomics
Backward compatibility matters: Even with major V2 changes, minimize breaking changes where possible
The Pydantic story demonstrates how a tool built to scratch a personal itch, combined with good timing (type hints maturing), thoughtful design (developer-friendly coercion), and key ecosystem adoption (FastAPI), can become foundational infrastructure—leading to a multi-year rewrite focused on performance, correctness, and extensibility.